Towards a new neurological model of language that explains outcome after stroke.

建立一种新的语言神经学模型来解释中风后的结果。

• 批准号: MR/M023672/1
• 负责人: Cathy Price
• 金额: $ 77.14万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FM023672%2F1
• 关键词: Towards; new; neurological; model; language

Every five minutes one person in the UK has a stroke. More than a third of these patients will develop aphasia, which can impair the ability to speak, understand speech, read and/or write. Aphasia is the most feared outcome after stroke because it severely impairs quality of life and makes stroke patients prone to social isolation, dependence and depression. One of the most demoralizing outcomes for aphasic patients is the lack of any information on their likely prognosis for recovery. This is a consequence of there being no established way to predict when, how or whether language skills will recover, which in turn makes it difficult to decide which course of therapy is best suited to an individual patient. Our research aims to Predict Language Outcome and Recovery After Stroke (PLORAS, http://www.ucl.ac.uk/ploras). In pursuit of this goal, we generating a database that records as many details as possible about hundreds of different stroke patients. Our initial investigations show that we can make confident predictions for language outcome for many patients. For example, we have identified 2 brain areas where the presence or absence of damage (caused by the stroke) very reliably predicts whether or not a patient will have "persistent" difficulty producing speech (i.e. years after stroke). These findings are important to share with new patients and their carers as they help to provide realistic expectations of likely recovery. Prognoses of good outcome are valuable because they substantially reduce anxiety about the future. Prognoses of poor outcome are more challenging for patients and their carers to deal with in the short term, but they can ease future frustration and depression associated with a failure to meet unrealistic targets.The aim of the proposed research is to provide a scientific explanation of how the predictions are working. This comes in the form of what we refer to as a "neurological model" of language that predicts speech and language outcome after stroke by identifying which brain regions are critical to language, the functions they support (e.g. speech comprehension or reading) and how the regions connect to one another. The current neurological model of language, illustrated in medical textbooks today, is based on crude post mortem data from less than a handful of aphasic patients tested at the end of the 19th Century by Paul Broca (1861) and Karl Wernicke (1874). Despite its enduring popularity, this model has not stood the test of time because it does not reliably predict outcome in new patients. Thus, although the classic neurological model is conceptually appealing, it does not make predictions that are accurate enough to be clinically useful. Conversely, our data-led analyses are starting to make very accurate predictions but we do not understand their scientific basis. Our proposal aims to provide a new neurological model that can be used to explain and ultimately improve the predictions we make. The new neurological model will illustrate the set of brain regions that support different language abilities (e.g. the ability to speak or understand speech). These brain regions will correspond to the parts of the brain that are typically damaged in patients who have selective difficulty with one particular language skill. In order to find these areas, and demonstrate how consistently they affect other patients, we have developed a completely new method for analysing the exact location and extent of brain damage. Our studies also place great importance on how much time, since the stroke, the patient has had to recover. We also aim to investigate the influence of many other factors on a patient's recovery, including the amount of speech therapy they have been given.

在英国，每五分钟就有一个人中风。超过三分之一的患者将发展为失语症，这可能会损害说话，理解演讲，阅读和/或写作的能力。失语症是中风后最可怕的结果，因为它严重损害生活质量，使中风患者容易社交孤立，依赖和抑郁。失语症患者最令人沮丧的结果之一是缺乏任何关于他们可能的康复预后的信息。这是因为没有既定的方法来预测语言技能何时、如何或是否会恢复，这反过来又使得难以决定哪种治疗过程最适合个体患者。我们的研究旨在预测中风后的语言结果和恢复（PLORAS，http：//www.ucl.ac.uk/ploras）。为了实现这一目标，我们建立了一个数据库，尽可能多地记录了数百名不同中风患者的详细信息。我们的初步调查表明，我们可以对许多患者的语言结果做出可靠的预测。例如，我们已经确定了两个大脑区域，其中存在或不存在损伤（由中风引起）非常可靠地预测患者是否会有“持续”的言语困难（即中风后数年）。这些发现与新患者及其护理人员分享很重要，因为它们有助于提供可能恢复的现实期望。良好结果的预测是有价值的，因为它们大大减少了对未来的焦虑。对于患者和他们的护理人员来说，短期内处理不良结果的预测更具挑战性，但它们可以减轻未来与未能实现不切实际的目标相关的挫折感和抑郁症。拟议研究的目的是为预测如何工作提供科学解释。这以我们所说的语言“神经模型”的形式出现，它通过识别哪些大脑区域对语言至关重要，它们支持的功能（例如语音理解或阅读）以及这些区域如何相互连接来预测中风后的语言和语言结果。当今医学教科书中描述的语言的神经学模型，是基于世纪末保罗·布罗卡（Paul Broca，1861）和卡尔·韦尼克（Karl Wernicke，1874）对少数失语症患者进行的粗略尸检数据。尽管该模型一直很受欢迎，但它并没有经受住时间的考验，因为它不能可靠地预测新患者的结果。因此，尽管经典的神经模型在概念上很有吸引力，但它并不能做出足够准确的临床预测。相反，我们以数据为主导的分析开始做出非常准确的预测，但我们不了解它们的科学依据。我们的建议旨在提供一种新的神经模型，可用于解释并最终改善我们所做的预测。新的神经模型将展示支持不同语言能力（例如说话或理解语音的能力）的大脑区域。这些大脑区域将对应于那些在特定语言技能方面有选择性困难的患者大脑中通常受损的部分。为了找到这些区域，并证明它们如何持续影响其他患者，我们开发了一种全新的方法来分析脑损伤的确切位置和程度。我们的研究也非常重视中风后患者需要多长时间才能康复。我们还旨在调查许多其他因素对患者康复的影响，包括他们接受的语言治疗量。

项目成果

Evidence for shared conceptual representations for sign and speech

符号和言语共享概念表征的证据

• DOI: 10.1101/623645
• 发表时间: 2019
• 作者: Evans S

Inflated Estimates of Proportional Recovery From Stroke: The Dangers of Mathematical Coupling and Compression to Ceiling.

• DOI: 10.1161/strokeaha.120.033031
• 发表时间: 2021-05
• 期刊: Stroke
• 影响因子: 8.3
• 作者: Bowman H;Bonkhoff A;Hope T;Grefkes C;Price C
• 通讯作者: Price C

Efficacy of spoken word comprehension therapy in patients with chronic aphasia: a cross-over randomised controlled trial with structural imaging.

• DOI: 10.1136/jnnp-2020-324256
• 发表时间: 2020-11-05
• 期刊: Journal of neurology, neurosurgery, and psychiatry
• 作者: Fleming V;Brownsett S;Krason A;Maegli MA;Coley-Fisher H;Ong YH;Nardo D;Leach R;Howard D;Robson H;Warburton E;Ashburner J;Price CJ;Crinion JT;Leff AP
• 通讯作者: Leff AP

A functional dissociation of the left frontal regions that contribute to single word production tasks.

• DOI: 10.1016/j.neuroimage.2021.118734
• 发表时间: 2021-12-15
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Ekert JO;Lorca-Puls DL;Gajardo-Vidal A;Crinion JT;Hope TMH;Green DW;Price CJ
• 通讯作者: Price CJ

Using multi-level Bayesian lesion-symptom mapping to probe the body-part-specificity of gesture imitation skills.

• DOI: 10.1016/j.neuroimage.2017.08.036
• 发表时间: 2017-11-01
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Achilles EIS;Weiss PH;Fink GR;Binder E;Price CJ;Hope TMH
• 通讯作者: Hope TMH